Best AI Model for Coding.

the ranking

the top picks, decoded

1. Claude Opus 5 — 97.0% SWE-bench Verified

The highest independently measured coding score on the board at 97.0%, at half the price of Fable 5. Strongest on short and medium tasks, though GPT-5.6 Sol still edges it on multi-hour work. Independent (vals.ai, observed Jul 25 2026, mini-swe-agent bash-only harness): SWE-bench Verified 97.00% ±0.76, the highest score on the board and 1st of the 75 systems vals.ai has run. Entered ranked Jul 25, 2026 after one day unranked: Anthropic published no SWE-bench Verified number at launch and still has not, so this is vals.ai's own measurement rather than a vendor claim. Read the #1 as a three-way tie, not a win — GPT-5.6 Sol is at 96.20% ±0.86 (a 0.8-point gap, ~0.7 sigma) and Claude Fable 5 at 95.00% ±0.98 (2.0 points, ~1.6 sigma), both inside the combined margin of error. We rank Opus 5 first only because it scored highest. Where the top two genuinely separate is task length, and not in Opus 5's favour: on the 1-to-4-hour tier Sol solves 98% against Opus 5's 90%, while Opus 5 leads on shorter work (98% under 15 minutes and 97% on 15-minute-to-1-hour tasks, vs 97% and 95% for Sol). Released Jul 24, 2026 at $5/$25 per 1M, the same price as Opus 4.8 and half of Fable 5. Anthropic's launch claims stay unreproducible (Frontier-Bench v0.1, CursorBench 3.2 and Zapier AutomationBench are proprietary), so this is the first externally checkable score the model has. Fast mode runs about 2.5x default speed at 2x base price.

2. GPT-5.6 Sol — 96.2% SWE-bench Verified

The strongest model on long tasks: 98% on the 1-to-4-hour tier, ahead of Claude Opus 5, and the second-highest overall score. Independent (vals.ai, Jul 14 2026, mini-swe-agent bash-only harness): SWE-bench Verified 96.20% ±0.86 — now the second-highest score on the board. Correction, Jul 25, 2026: this row read "the top score on the board" from Jul 17 until Jul 25, when vals.ai evaluated Claude Opus 5 at 97.00% ±0.76 and took the #1 slot. The 0.8-point gap is ~0.7 sigma and not significant, so the two are a statistical tie, and Sol still leads on the longest tasks (98% vs 90% on the 1-to-4-hour tier). Verified Jul 17, 2026; it had been unranked since Jun 26 because OpenAI published no SWE-bench number of its own, and it still has not. Read the #1 with care: the 1.2-point lead over Claude Fable 5 (95.00% ±0.98) is inside the combined margin of error (~0.9 sigma, not significant), so the two are a statistical tie and we rank Sol first only because it scored higher. Where it does separate is task length — 98% on 1-4 hour tasks vs 93% for Fable 5. OpenAI's own Terminal-Bench 2.1 claim is 88.8% (Sol) / 91.9% (Sol Ultra). No SWE-bench Pro score published. Pricing $5/$30 per 1M.

3. Grok 4.6 — 95.6% SWE-bench Verified

A post-training refresh of Grok 4.5 that lands in the top group on the neutral harness, at a third the price of the models around it. Independent (vals.ai, benchmark updated 2026-08-12, mini-swe-agent bash-only harness): SWE-bench Verified 95.60% ±0.92. Entered ranked 2026-08-13, one day after launch, and it enters near the top: 4th of the 82 systems vals.ai has run, behind Claude Opus 5 (97.00% ±0.76), DeepSeek V4 Pro 0813 (96.40% ±0.83) and GPT-5.6 Sol (96.20% ±0.86). Read the gap to Claude Fable 5 below it (95.00% ±0.98) as a tie: 0.6 points against a pooled SEM of about 1.34 is well inside the margin of error, and we rank Grok 4.6 higher only because it scored higher. Same against Sol above it, a 0.6-point gap. This is the second time running that xAI shipped a Grok with no SWE-bench Verified number of its own and vals.ai supplied one: Grok 4.5 waited nine days, Grok 4.6 waited one. SpaceXAI still publishes no SWE-bench Verified or SWE-bench Pro figure, so the ranked score here is entirely vals.ai's measurement with no vendor claim to disclose against. What xAI did publish, all vendor-reported on its own table: AA Intelligence Index 61, CursorBench v3.2 69.9%, DeepSWE v1.1 65.9%, FrontierCode v1.1 Extended 61.3%, APEX-Agents 57.5%, APEX-SWE 56.4%, Terminal-Bench v3.0 26%, GDPVal-AA v2 1753, AA-Briefcase 1577, Harvey LAB (Vals) 15.8%. That table's shape still holds and is worth reading against this score: Grok 4.6 takes the best result on the three knowledge-work evals and loses the two hardest agentic-coding ones by wide margins (DeepSWE 65.9% vs Sol's 73%, Terminal-Bench v3.0 26% vs Sol's 34.6%). Terminal-Bench v3.0 is NOT comparable to the 83.3% TB2.1 figure on the Grok 4.5 row: on v3.0 Grok 4.5 scores 15.7% and the whole field tops out near 34%. On cost it is the standout of the top five, $0.78 per test against $1.29 for Opus 5, $1.15 for Sol and $2.05 for Fable 5, though it is also the slowest of them at 604s. Pricing unchanged from Grok 4.5 at $2/$6 per 1M. See /p/grok-4-6-frontier-benchmarks-terminal-bench-gap/.

how we rank

We rank by SWE-bench Verified (500 real, human-validated GitHub issues resolved end-to-end), tiebroken by the harder SWE-bench Pro. A score is only printed once confirmed against an independent evaluation or the maker's primary source — and every row states which kind it is. Where both exist, we print both: one as the ranked score, the other in that row's note. We would rather show you the gap than ask you to trust our pick. Our independent reference is vals.ai, which runs every model itself through the same minimal bash-only harness (mini-swe-agent), so the models are compared on equal footing. That matters more than it sounds: SWE-bench scores a model and its scaffolding together, and vendors report using their own tuned scaffolds. Against vals.ai's neutral harness, the vendor claims on this board run 2.6 to 11.6 points optimistic. So rows marked vendor-reported are best-case numbers and are not strictly comparable to the independent ones — where we know the independent figure, we print it in the row's note. That is a deliberate choice and you should know we made it: on four rows (Claude Sonnet 5, MiniMax M3, Qwen3.7 Max, Kimi K2.6) an independent score for that exact model exists and is lower, and we still rank on the maker's published figure because it is the number that model is sold and quoted on. We disclose the independent one beside it instead of quietly restating the board on a single evaluator's harness choice. The honest consequence: positions that straddle the two regimes are approximate. Qwen3.7 Max is the sharpest case — it sits at #16 on Alibaba's 80.4%, and on the neutral harness its 68.8% would put it far down the table. Note that llm-stats, which we previously miscredited as an independent tracker, labels its own SWE-bench Verified table "Verified: 0 / Self-reported: 104"; it aggregates vendor claims. Models still being checked are marked “verifying” and shown without a number rather than estimated. Prices are per 1M input tokens on the standard API tier and can change — always confirm current pricing with the provider.

Want the raw numbers? The full dataset is public: JSON · CSV.

From our full AI Coding Leaderboard (2026-08-17). We only rank scores confirmed against primary sources.